How to extrapolate data from data

There are many occasions when a column of data needs to be created from an already existing column for ease of data manipulation. For example, perhaps you have a body of text as a pathology report and you want to extract all the reports where the diagnosis is ‘dysplasia’.

You could just subset the data using grepl so that you only get the reports that mention this word…but what if the data needs to be cleaned prior to subsetting like excluding reports where the diagnosis is normal but the phrase ‘No evidence of dysplasia’ is present. Or perhaps there are other manipulations needed prior to subsetting.

This is where data accordionisation is useful. This simply means the creation of data from (usually) a column into another column in the same dataframe.

The neatest way to do this is with the mutate function from the {dplyr} package which is devoted to data cleaning. There are also other ways which I will demonstrate at the end.

The input data here will be an endoscopy data set:

Age <- sample(1:100, 130, replace = TRUE)
Dx <- sample(c("NDBE", "LGD", "HGD", "IMC"), 130, replace = TRUE)
TimeOfEndoscopy <- sample(1:60, 130, replace = TRUE)

library(dplyr)

EMRdf <- data.frame(Age, Dx, TimeOfEndoscopy, stringsAsFactors = F)

Perhaps you need to calculate the number of hours spent doing each endoscopy rather than the number of minutes

EMRdftbb <- EMRdf %>% mutate(TimeOfEndoscopy / 60)

# install.packages("knitr")
library(knitr)
library(kableExtra)

# Just show the top 20 results

kable(head(EMRdftbb, 20))

Age	Dx	TimeOfEndoscopy	TimeOfEndoscopy/60
87	NDBE	24	0.4000000
41	NDBE	48	0.8000000
15	LGD	40	0.6666667
16	HGD	19	0.3166667
38	IMC	22	0.3666667
87	IMC	52	0.8666667
29	HGD	55	0.9166667
20	IMC	45	0.7500000
83	NDBE	45	0.7500000
97	HGD	22	0.3666667
17	NDBE	50	0.8333333
81	NDBE	16	0.2666667
39	NDBE	40	0.6666667
43	HGD	34	0.5666667
12	IMC	56	0.9333333
62	NDBE	11	0.1833333
68	LGD	51	0.8500000
37	LGD	35	0.5833333
92	LGD	7	0.1166667
38	IMC	57	0.9500000

That is useful but what if you want to classify the amount of time spent doing each endoscopy as follows: <0.4 hours is too little time and >0.4 hours is too long.

Using ifelse() with mutate for conditional accordionisation.

For this we would use ifelse(). However this can be combined with mutate() so that the result gets put in another column as follows

EMRdf2 <- EMRdf %>%
  mutate(TimeInHours = TimeOfEndoscopy / 60) %>%
  mutate(TimeClassification = ifelse(TimeInHours > 0.4, "Too Long", "Too Short"))

# Just show the top 20 results

kable(head(EMRdf2, 20))

Age	Dx	TimeOfEndoscopy	TimeInHours	TimeClassification
87	NDBE	24	0.4000000	Too Short
41	NDBE	48	0.8000000	Too Long
15	LGD	40	0.6666667	Too Long
16	HGD	19	0.3166667	Too Short
38	IMC	22	0.3666667	Too Short
87	IMC	52	0.8666667	Too Long
29	HGD	55	0.9166667	Too Long
20	IMC	45	0.7500000	Too Long
83	NDBE	45	0.7500000	Too Long
97	HGD	22	0.3666667	Too Short
17	NDBE	50	0.8333333	Too Long
81	NDBE	16	0.2666667	Too Short
39	NDBE	40	0.6666667	Too Long
43	HGD	34	0.5666667	Too Long
12	IMC	56	0.9333333	Too Long
62	NDBE	11	0.1833333	Too Short
68	LGD	51	0.8500000	Too Long
37	LGD	35	0.5833333	Too Long
92	LGD	7	0.1166667	Too Short
38	IMC	57	0.9500000	Too Long

Note how we can chain the mutate() function together.

Using multiple ifelse()

What if we want to get more complex and put several classifiers in? We just use more ifelse’s:

EMRdf2 <- EMRdf %>%
  mutate(TimeInHours = TimeOfEndoscopy / 60) %>%
  mutate(TimeClassification = ifelse(TimeInHours > 0.8, "Too Long", ifelse(TimeInHours < 0.5, "Too Short", ifelse(TimeInHours >= 0.5 & TimeInHours <= 0.8, "Just Right", "N"))))

# Just show the top 20 results

kable(head(EMRdf2, 20))

Age	Dx	TimeOfEndoscopy	TimeInHours	TimeClassification
87	NDBE	24	0.4000000	Too Short
41	NDBE	48	0.8000000	Just Right
15	LGD	40	0.6666667	Just Right
16	HGD	19	0.3166667	Too Short
38	IMC	22	0.3666667	Too Short
87	IMC	52	0.8666667	Too Long
29	HGD	55	0.9166667	Too Long
20	IMC	45	0.7500000	Just Right
83	NDBE	45	0.7500000	Just Right
97	HGD	22	0.3666667	Too Short
17	NDBE	50	0.8333333	Too Long
81	NDBE	16	0.2666667	Too Short
39	NDBE	40	0.6666667	Just Right
43	HGD	34	0.5666667	Just Right
12	IMC	56	0.9333333	Too Long
62	NDBE	11	0.1833333	Too Short
68	LGD	51	0.8500000	Too Long
37	LGD	35	0.5833333	Just Right
92	LGD	7	0.1166667	Too Short
38	IMC	57	0.9500000	Too Long

Using multiple ifelse() with grepl() or string_extract

Of course we need to extract information from text as well as numeric data. We can do this using grepl() or string_extract() from the library(stringr).

Let’s say we want to extract all the samples that had IMC. We don’t want to subset the data, just extract IMC into a column that says IMC and the rest say ’Non-IMC’

Using the dataset above:

library(stringr)

EMRdf$MyIMC_Column <- str_extract(EMRdf$Dx, "IMC")

# to fill the NA's we would do:EMRdf$MyIMC_Column<-ifelse(grepl("IMC",EMRdf$Dx),"IMC","NoIMC")

# Another way to do this (really should be for more complex examples when you want to extract the entire contents of the cell that has the match)

EMRdf$MyIMC_Column <- ifelse(grepl("IMC", EMRdf$Dx), str_extract(EMRdf$Dx, "IMC"), "NoIMC")

So data can be usefully created from data for further analysis.

Hopefully this way of extrapolating data and especially using conditional expressions to categorise data according to some rules is a helpful way to get more out of your data.

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This article originally appeared on https://sebastiz.github.io/gastrodatascience/ and has been edited to render in Quarto and had NHS-R styles applied.